Summary: This project concerns a problem at the heart of systems biology: the reconstruction of molecular regulatory networks from system-level experimental observations. While there are many algorithms available that aim to infer network structure from experimental data, less emphasis has been placed on methods that utilize time series data effectively to infer both, structure and dynamical models of networks.
We propose an inference algorithm within the Boolean polynomial dynamical system (BPDS) framework. The algorithm is able to use time-course data, including network perturbations such as knock-out mutants and RNAi experiments. To infer wiring diagrams and dynamical models, it allows for the incorporation of prior biological knowledge, and it is robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization withal an encoding of models as BPDS. This BPDS framework allows an effective representation of the models needed to be searched by our algorithm to improve computational performance. We validated the algorithm both in silico network and on microarray expression profiles.
Link to paper: P. Vera-Licona, A. Jarrah, LD. Garcia, J. McGee, R. Laubenbacher. An Algebra-Based Method for Inferring Gene Regulatory Networks.